n mix results

N - mixture models by species

modelling N by site to get relative abundance

abundance by site will be used as a cov on predator occupancy

7 models evaluated, dot, jdt + jdtSQ, lure + jdt + jdtSQ

Species:      YellowBelliedMarmot

Metadata Summary:

N_sites	N_counts	N_detections	rep_period	iterations	burnin	thin
127	496	167	7 days	120000	20000	10

Detections by Year:

Yr	2016	2017	2018	2019	2020
sites	19	31	19	32	26
detections	13	63	17	50	24
N.dot.model	4	15	10	54	41

WAIC

Models by WAIC:

model	description	WAIC	N.total.est
fm7	counts	13.78466	127
fm4	lure + jdt	825.63785	144
fm6	lure + jdt + jdtSq	830.08352	144
fm2	jdt	841.75801	99
fm5	jdt + jdtSq	846.14263	101
fm1	dot	1096.23128	124
fm3	lure	1117.58635	106

Model summaries:

model: fm1

dot

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
p[1]	NA	8149	0.151	0.15	0.08	0.22	0	1.001
p[2]	NA	7043	0.163	0.159	0.12	0.20	0	1.001
p[3]	NA	3588	0.034	0.031	0.01	0.06	0	1.0009
p[4]	NA	1347	0.033	0.031	0.01	0.05	0	1.0009
p[5]	NA	704	0.021	0.015	0.00	0.04	0	0.9981
lambda[1]	NA	9289	0.238	0.191	0.06	0.41	0	1.0007
lambda[2]	NA	9517	0.545	0.5	0.29	0.78	0	1.001
lambda[3]	NA	1875	0.905	0.714	0.30	1.46	0	1.001
lambda[4]	NA	441	2.042	1.458	0.80	3.15	0	1.0009
lambda[5]	NA	219	2.313	0.993	0.37	4.44	0	1.0011
N[93]	NA	1262	2.206	1	1.00	4.00	0	1.0006
N[67]	NA	3061	3.320	2.994	2.00	5.00	0	1.001
N[94]	NA	1186	2.931	2.002	1.00	5.00	0	1
N[6]	NA	9301	0.016	err	0.00	0.00	err	err
N[7]	NA	9323	0.019	err	0.00	0.00	err	err

p[1]

p[2]

p[3]

p[4]

p[5]

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[93]

N[67]

N[94]

N[6]

N[7]

model: fm2

jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	jdt	1729	2.131	2.127	1.84	2.43	0	1.001
alpha0	NA	1555	-4.439	-4.426	-4.84	-4.00	0	1.001
lambda[1]	NA	6993	0.438	0.326	0.12	0.74	0	1.0008
lambda[2]	NA	6067	1.226	1.119	0.68	1.77	0	1.001
lambda[3]	NA	5764	0.941	0.775	0.41	1.41	0	1.001
lambda[4]	NA	8725	0.968	0.899	0.62	1.31	0	1.001
lambda[5]	NA	6517	1.042	1.014	0.57	1.54	0	1.001
N[70]	NA	10000	0.940	0	0.00	2.00	1	err
N[115]	NA	9429	1.375	1	1.00	2.00	0	1
N[25]	NA	8888	0.159	err	0.00	1.00	err	err
N[91]	NA	8260	1.308	1	1.00	2.00	0	1
N[55]	NA	9635	0.307	0	0.00	1.00	1	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[70]

N[115]

N[25]

N[91]

N[55]

alpha relationship

model: fm3

lure

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	lure	7925	0.111	0.108	-0.03	0.25	0.45582	0.8975
alpha0	NA	2938	-2.947	-2.897	-3.19	-2.66	0	1.001
lambda[1]	NA	6706	0.491	0.346	0.13	0.83	0	1.0008
lambda[2]	NA	4063	1.618	1.415	0.92	2.31	0	1.001
lambda[3]	NA	8753	0.626	0.575	0.28	0.94	0	1.001
lambda[4]	NA	4719	1.245	1.143	0.77	1.70	0	1.001
lambda[5]	NA	7793	0.672	0.625	0.35	0.98	0	1.001
N[76]	NA	8238	2.143	1.999	1.00	3.00	0	1.0006
N[47]	NA	7902	2.167	1	1.00	3.00	0	1.0006
N[94]	NA	8644	1.954	2.003	1.00	3.00	0	1.0005
N[65]	NA	9433	1.323	1	1.00	2.00	0	1
N[41]	NA	7868	0.271	err	0.00	1.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[76]

N[47]

N[94]

N[65]

N[41]

alpha relationship

model: fm4

lure + jdt

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	1747	-0.649	-0.659	-0.86	-0.42	0	1.001
alpha[2]	julianDt	1008	2.513	2.503	2.18	2.87	0	1.001
alpha0	NA	964	-4.769	-4.728	-5.24	-4.27	0	1.001
lambda[1]	NA	9701	0.321	0.228	0.09	0.55	0	1.0008
lambda[2]	NA	8080	0.917	0.842	0.50	1.32	0	1.001
lambda[3]	NA	7673	0.797	0.71	0.36	1.19	0	1.001
lambda[4]	NA	8268	1.018	0.958	0.64	1.37	0	1.001
lambda[5]	NA	1453	3.206	2.439	1.23	5.14	0	1.001
N[62]	NA	7131	1.192	err	1.00	2.00	err	err
N[31]	NA	9461	0.014	err	0.00	0.00	err	err
N[11]	NA	10000	0.054	err	0.00	0.00	err	err
N[70]	NA	10000	1.012	0	0.00	2.00	1	err
N[61]	NA	9658	0.183	err	0.00	1.00	err	err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[62]

N[31]

N[11]

N[70]

N[61]

model: fm5

jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	julianDt	141	2.997	2.84	1.97	4.16	0	1.001
alpha[2]	julianDtSq	156	-0.435	-0.406	-0.96	0.10	0.42233	0.9072
alpha0	NA	158	-4.869	-4.766	-5.51	-4.14	0	1.001
lambda[1]	NA	8056	0.423	0.312	0.11	0.71	0	1.0008
lambda[2]	NA	5761	1.243	1.117	0.68	1.77	0	1.001
lambda[3]	NA	6722	0.915	0.776	0.40	1.37	0	1.001
lambda[4]	NA	8269	0.981	0.933	0.62	1.32	0	1.001
lambda[5]	NA	5687	1.016	0.951	0.53	1.47	0	1.001
N[51]	NA	8846	0.546	0	0.00	2.00	1	err
N[74]	NA	8743	0.063	err	0.00	0.00	err	err
N[5]	NA	8542	1.923	2.002	1.00	3.00	0	1.0005
N[122]	NA	7869	2.015	1	1.00	3.00	0	1.0005
N[113]	NA	9708	0.353	0	0.00	1.00	1	err

alpha[1]

alpha[2]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[51]

N[74]

N[5]

N[122]

N[113]

julian date relationship

model: fm6

lure + jdt + jdtSq

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha[1]	lureDays	1843	-0.647	-0.607	-0.86	-0.41	0	1.001
alpha[2]	julianDt	215	2.668	2.522	1.77	3.58	0	1.001
alpha[3]	julianDtSq	296	-0.075	-0.045	-0.55	0.36	0.97918	0.5954
alpha0	NA	372	-4.852	-4.756	-5.48	-4.24	0	1.001
lambda[1]	NA	9067	0.319	0.228	0.09	0.55	0	1.0007
lambda[2]	NA	7735	0.911	0.858	0.49	1.30	0	1.001
lambda[3]	NA	7503	0.798	0.707	0.37	1.22	0	1.001
lambda[4]	NA	7153	1.022	0.948	0.65	1.39	0	1.001
lambda[5]	NA	1409	3.209	2.263	1.24	5.20	0	1.001
N[44]	NA	10000	0.908	0	0.00	2.00	1	err
N[29]	NA	8185	0.896	0	0.00	2.00	1	err
N[34]	NA	10000	0.075	err	0.00	0.00	err	err
N[20]	NA	10000	0.890	0	0.00	2.00	1	err
N[78]	NA	10000	0.997	0	0.00	2.00	1	err

alpha[1]

alpha[2]

alpha[3]

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[44]

N[29]

N[34]

N[20]

N[78]

model: fm7

counts

summary table

param	covariate	iters_ef	mean	mode	hdi_89pct_lower	hdi_89pct_upper	bayes_P	pct.density
alpha	counts	7181	13.500	13	11.46	15.50	0	1.001
alpha0	NA	8457	-5.884	-5.739	-6.89	-4.88	0	1.001
lambda[1]	NA	7007	0.931	0.642	0.22	1.56	0	1.0008
lambda[2]	NA	9303	0.977	0.872	0.51	1.39	0	1.001
lambda[3]	NA	10000	0.975	0.872	0.41	1.46	0	1.001
lambda[4]	NA	10000	0.998	0.944	0.65	1.34	0	1.001
lambda[5]	NA	9483	0.982	0.928	0.51	1.39	0	1.001
N[37]	NA	9578	0.970	0	0.00	2.00	1	err
N[53]	NA	0	1.000	err	1.00	1.00	err	err
N[94]	NA	0	1.000	err	1.00	1.00	err	err
N[88]	NA	10000	0.974	0	0.00	2.00	1	err
N[50]	NA	0	1.000	err	1.00	1.00	err	err

alpha

alpha0

lambda[1]

lambda[2]

lambda[3]

lambda[4]

lambda[5]

N[37]

N[53]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[94]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

N[88]

N[50]

## Warning in cor(X, use = "pairwise.complete.obs"): the standard deviation is zero

## Warning: Removed 50 rows containing missing values (geom_bar).

alpha relationship